

'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase  :		str  =		logging.get_logger(__name__)

_UpperCAmelCase  :		str  =		{
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'xlm-roberta'







 def __init__(       self  , snake_case_=3_05_22  , snake_case_=7_68  , snake_case_=12  , snake_case_=12  , snake_case_=30_72  , snake_case_="gelu"  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=5_12  , snake_case_=2  , snake_case_=0.02  , snake_case_=1E-12  , snake_case_=1  , snake_case_=0  , snake_case_=2  , snake_case_="absolute"  , snake_case_=True  , snake_case_=None  , **snake_case_  , ):
   super().__init__(pad_token_id=snake_case_  , bos_token_id=snake_case_  , eos_token_id=snake_case_  , **snake_case_   )

   lowercase									=vocab_size
   lowercase									=hidden_size
   lowercase									=num_hidden_layers
   lowercase									=num_attention_heads
   lowercase									=hidden_act
   lowercase									=intermediate_size
   lowercase									=hidden_dropout_prob
   lowercase									=attention_probs_dropout_prob
   lowercase									=max_position_embeddings
   lowercase									=type_vocab_size
   lowercase									=initializer_range
   lowercase									=layer_norm_eps
   lowercase									=position_embedding_type
   lowercase									=use_cache
   lowercase									=classifier_dropout

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 @property
 def 						_A(       self   ):
   if self.task == "multiple-choice":
     lowercase									={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
   else:
     lowercase									={0: '''batch''', 1: '''sequence'''}
   return OrderedDict(
       [
           ('''input_ids''', dynamic_axis),
           ('''attention_mask''', dynamic_axis),
       ]   )






'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)

@dataclass
class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Union[List[PIL.Image.Image], np.ndarray]
 __snake_case:							Optional[List[bool]]
 __snake_case:							Optional[List[bool]]


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker


